Contributions to Distributed Spatial Compression in Wireless Sensor Networks

Projecte final de carrera fet en col.laboració amb University of Southern CaliforniaPremi Càtedra Red.es en l’Àrea de Sistemes de la Informació al millor Projecte de Fi de Carrera d'Enginyeria de Telecomunicació. Atorgat per Càtedra Red.es. (Curs 2010-2011)This thesis presents several contributions in the field of distributed spatial compression inWireless Sensor Networks. First, since in most of the spatial compression schemes some nodes (raw nodes) need to broadcast their raw data to allow other nodes (aggregating nodes) to perform compression, we design several distributed heuristics which, via local communications, split the nodes into raw/aggregating subsets and optimize the amount of energy consumed in the network. We also extend previous work in the use of graph-based lifting transforms for data compression in distributed data gathering applications, to networks with more than one sink, and scenarios where all data has to be available at every node. Additionally, under the scope of these contributions, we design a new energy-efficient multicast routing algorithm, which is based on the minimum Steiner tree and exploits the broadcast property of wireless communications. We prove via computer-based simulations that our methods reduce the energy consumption in the network in comparison with existing approaches.Award-winnin

Unified Power Management in Wireless Sensor Networks, Doctoral Dissertation, August 2006

Radio power management is of paramount concern in wireless sensor networks (WSNs) that must achieve long lifetimes on scarce amount of energy. Previous work has treated communication and sensing separately, which is insufficient for a common class of sensor networks that must satisfy both sensing and communication requirements. Furthermore, previous approaches focused on reducing energy consumption in individual radio states resulting in suboptimal solutions. Finally, existing power management protocols often assume simplistic models that cannot accurately reflect the sensing and communication properties of real-world WSNs. We develop a unified power management approach to address these issues. We first analyze the relationship between sensing and communication performance of WSNs. We show that sensing coverage often leads to good network connectivity and geographic routing performance, which provides insights into unified power management under both sensing and communication performance requirements. We then develop a novel approach called Minimum Power Configuration that ingegrates the power consumption in different radio states into a unified optimization framework. Finally, we develop two power management protocols that account for realistic communication and sensing properties of WSNs. Configurable Topology Control can configure a network topology to achieve desired path quality in presence of asymmetric and lossy links. Co-Grid is a coverage maintenance protocol that adopts a probabilistic sensing model. Co-Grid can satisfy desirable sensing QoS requirements (i.e., detection probability and false alarm rate) based on a distributed data fusion model

Integer Programming Formulations for the Shared Multicast Tree Problem

We study the shared multicast tree (SMT) problem in wireless networks. To support a multicast session between a set of network nodes, SMT aims to establish a wireless connection between them, such that the total energy consumption is minimized. All destinations of the multicast message must be connected, while non-destinations are optional nodes that can be used to relay messages. The objective function reflecting power consumption distinguishes SMT clearly from the traditional minimum Steiner tree problem. We develop two integer programming formulations for SMT. Both models are subsequently extended and strengthened. Theorems on relations between the LP bounds corresponding to the models are stated and proved. As the number of variables in the strongest formulations is a polynomial of degree four in the number of network nodes, the models are impractical for computing lower bounds in instances beyond a fairly small size, and therefore a constraint generation scheme is developed. Results from computational experiments with the models demonstrate good promise of the approaches taken.acceptedVersio

Impacts of Channel Switching Overhead on the Performance of Multicast in Wireless Mesh Networks

Wireless mesh networks (WMNs) have emerged as a promising technology for next generation wireless networking. A WMN extends network coverage using wireless mesh routers that communicate with each other via multi-hop wireless communications. One technique to increase the network capacity of WMNs is to use routers equipped with multiple radios capable of transmitting and receiving on multiple channels. In a Multi-Channel Multi-Radio wireless mesh network (MCMR WMN), nodes are capable of transmitting and receiving data simultaneously through different radios and at least theoretically doubling the average throughput. On the other hand, the use of multi-radio and multi-channel technology in many cases requires routers to switch channels for each transmission and/or reception. Channel switching incurs additional costs and delay. In this thesis, we present a simulation-based study of the impacts of channel switching overheads on the performance of multicast in MCMR WMNs. We study how channel switching overheads affect the performance metrics such as packet delivery ratio, throughput, end-to-end delay, and delay jitter of a multicast session. In particular, we examine: 1. the performance of multicast in MCMR WMNs with three orthogonal channels versus eleven overlapping channels defined in IEEE 802.11b. 2. the performance of the Minimum-interference Multi-channel Multi-radio Multicast (M4) algorithm with and without channel switching. 3. the performance of the Multi-Channel Minimum Number of Transmissions (MCMNT) algorithm (which does not do channel switching) in comparison with the M4 algorithm (which performs channel switching)

A survey on energy efficient techniques in wireless sensor networks

International audienceThe myriad of potential applications supported by wireless sensor networks (WSNs) has generated much interest from the research community. Various applications range from small size low industrial monitoring to large scale energy constrained environmental monitoring. In all cases, an operational network is required to fulfill the application missions. In addition, energy consumption of nodes is a great challenge in order to maximize network lifetime. Unlike other networks, it can be hazardous, very expensive or even impossible to charge or replace exhausted batteries due to the hostile nature of environment. Researchers are invited to design energy efficient protocols while achieving the desired network operations. This paper focuses on different techniques to reduce the consumption of the limited energy budget of sensor nodes. After having identified the reasons of energy waste in WSNs, we classify energy efficient techniques into five classes, namely data reduction, control reduction, energy efficient routing, duty cycling and topology control. We then detail each of them, presenting subdivisions and giving many examples. We conclude by a recapitulative table

Optimizing performance and energy efficiency of group communication and internet of things in cognitive radio networks

Data traffic in the wireless networks has grown at an unprecedented rate. While traditional wireless networks follow fixed spectrum assignment, spectrum scarcity problem becomes a major challenge in the next generations of wireless networks. Cognitive radio is a promising candidate technology that can mitigate this critical challenge by allowing dynamic spectrum access and increasing the spectrum utilization. As users and data traffic demands increases, more efficient communication methods to support communication in general, and group communication in particular, are needed. On the other hand, limited battery for the wireless network device in general makes it a bottleneck for enhancing the performance of wireless networks. In this thesis, the problem of optimizing the performance of group communication in CRNs is studied. Moreover, energy efficient and wireless-powered group communication in CRNs are considered. Additionally, a cognitive mobile base station and a cognitive UAV are proposed for the purpose of optimizing energy transfer and data dissemination, respectively. First, a multi-objective optimization for many-to-many communication in CRNs is considered. Given a many-to-many communication request, the goal is to support message routing from each user in the many-to-many group to each other. The objectives are minimizing the delay and the number of used links and maximizing data rate. The network is modeled using a multi-layer hyper graph, and the secondary users\u27 transmission is scheduled after establishing the conflict graph. Due to the difficulty of solving the problem optimally, a modified version of an Ant Colony meta-heuristic algorithm is employed to solve the problem. Additionally, energy efficient multicast communication in CRNs is introduced while considering directional and omnidirectional antennas. The multicast service is supported such that the total energy consumption of data transmission and channel switching is minimized. The optimization problem is formulated as a Mixed Integer Linear Program (MILP), and a heuristic algorithm is proposed to solve the problem in polynomial time. Second, wireless-powered machine-to-machine multicast communication in cellular networks is studied. To incentivize Internet of Things (IoT) devices to participate in forwarding the multicast messages, each IoT device participates in messages forwarding receives Radio Frequency (RF) energy form Energy Transmitters (ET) not less than the amount of energy used for messages forwarding. The objective is to minimize total transferred energy by the ETs. The problem is formulated mathematically as a Mixed Integer Nonlinear Program (MINLP), and a Generalized Bender Decomposition with Successive Convex Programming (GBD-SCP) algorithm is introduced to get an approximate solution since there is no efficient way in general to solve the problem optimally. Moreover, another algorithm, Constraints Decomposition with SCP and Binary Variable Relaxation (CDR), is proposed to get an approximate solution in a more efficient way. On the other hand, a cognitive mobile station base is proposed to transfer data and energy to a group of IoT devices underlying a primary network. Total energy consumed by the cognitive base station in its mobility, data transmission and energy transfer is minimized. Moreover, the cognitive base station adjusts its location and transmission power and transmission schedule such that data and energy demands are supported within a certain tolerable time and the primary users are protected from harmful interference. Finally, we consider a cognitive Unmanned Aerial Vehicle (UAV) to disseminate data to IoT devices. The UAV senses the spectrum and finds an idle channel, then it predicts when the corresponding primary user of the selected channel becomes active based on the elapsed time of the off period. Accordingly, it starts its transmission at the beginning of the next frame right after finding the channel is idle. Moreover, it decides the number of the consecutive transmission slots that it will use such that the number of interfering slots to the corresponding primary user does not exceed a certain threshold. A mathematical problem is formulated to maximize the minimum number of bits received by the IoT devices. A successive convex programming-based algorithm is used to get a solution for the problem in an efficiency way. It is shown that the used algorithm converges to a Kuhn Tucker point

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

The edge-disjoint path problem on random graphs by message-passing

We present a message-passing algorithm to solve the edge disjoint path problem (EDP) on graphs incorporating under a unique framework both traffic optimization and path length minimization. The min-sum equations for this problem present an exponential computational cost in the number of paths. To overcome this obstacle we propose an efficient implementation by mapping the equations onto a weighted combinatorial matching problem over an auxiliary graph. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behaviour of both the number of paths to be accommodated and their minimum total length.Comment: 14 pages, 8 figure